MTT: Multi-scale temporal transformer for skeleton-based action recognition
In the task of skeleton-based action recognition, long-term temporal dependencies are
significant cues for sequential skeleton data. State-of-the-art methods rarely have access to …
significant cues for sequential skeleton data. State-of-the-art methods rarely have access to …
Joint learning in the spatio-temporal and frequency domains for skeleton-based action recognition
Benefiting from its succinctness and robustness, skeleton-based action recognition has
recently attracted much attention. Most existing methods utilize local networks (eg recurrent …
recently attracted much attention. Most existing methods utilize local networks (eg recurrent …
Spatio-temporal tuples transformer for skeleton-based action recognition
H Qiu, B Hou, B Ren, X Zhang - arXiv preprint arXiv:2201.02849, 2022 - arxiv.org
Capturing the dependencies between joints is critical in skeleton-based action recognition
task. Transformer shows great potential to model the correlation of important joints …
task. Transformer shows great potential to model the correlation of important joints …
Spatial temporal graph attention network for skeleton-based action recognition
It's common for current methods in skeleton-based action recognition to mainly consider
capturing long-term temporal dependencies as skeleton sequences are typically long (> 128 …
capturing long-term temporal dependencies as skeleton sequences are typically long (> 128 …
Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition
Graph convolutional networks have been widely used for skeleton-based action recognition
due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a …
due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a …
Spatial residual layer and dense connection block enhanced spatial temporal graph convolutional network for skeleton-based action recognition
Recent research has shown that modeling the dynamic joint features of the human body by
a graph convolutional network (GCN) is a groundbreaking approach for skeleton-based …
a graph convolutional network (GCN) is a groundbreaking approach for skeleton-based …
Skeleton-based action recognition with synchronous local and non-local spatio-temporal learning and frequency attention
Benefiting from its succinctness and robustness, skeleton-based action recognition has
recently attracted much attention. Most existing methods utilize local networks (eg recurrent …
recently attracted much attention. Most existing methods utilize local networks (eg recurrent …
Skeleton-based action recognition via temporal-channel aggregation
S Wang, Y Zhang, M Zhao, H Qi, K Wang, F Wei… - arXiv preprint arXiv …, 2022 - arxiv.org
Skeleton-based action recognition methods are limited by the semantic extraction of spatio-
temporal skeletal maps. However, current methods have difficulty in effectively combining …
temporal skeletal maps. However, current methods have difficulty in effectively combining …
Feature reconstruction graph convolutional network for skeleton-based action recognition
J Huang, Z Wang, J Peng, F Huang - Engineering Applications of Artificial …, 2023 - Elsevier
Skeleton-based action recognition is an important task in computer vision. Recently, graph
convolutional networks (GCNs) have been successfully applied to this task and achieved …
convolutional networks (GCNs) have been successfully applied to this task and achieved …
Motif-GCNs with local and non-local temporal blocks for skeleton-based action recognition
Recent works have achieved remarkable performance for action recognition with human
skeletal data by utilizing graph convolutional models. Existing models mainly focus on …
skeletal data by utilizing graph convolutional models. Existing models mainly focus on …